(128 days)
Yes
The "Intended Use / Indications for Use" section explicitly states that the device is a "machine learning-based decision support system." The "Device Description" section further elaborates that the core engine uses "machine learning techniques" and draws upon "knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model."
No.
The device is a decision support software designed to assist physicians in analyzing images for diagnosis; it does not directly treat or prevent a disease or condition.
Yes
Explanation: The "Intended Use / Indications for Use" section explicitly states, "Koios DS for Breast is a machine learning-based decision support system, indicated as an adjunct to diagnostic ultrasound for breast cancer." It also mentions that the device "provides information that may be useful in rendering an accurate diagnosis." While it's an adjunct and not for primary diagnosis, its role is to assist in the diagnostic process.
Yes
The device description explicitly states that Koios DS for Breast is a "software application" and a "web application" that does not require any "specialized hardware." It functions by processing images transmitted electronically to a server and returning results to the user through a compatible client. While it interacts with DICOM images from other devices, the core medical device itself is the software.
Based on the provided information, yes, this device is an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use clearly states that the software is designed to "assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions" and provides "information that may be useful in rendering an accurate diagnosis." This aligns with the definition of an IVD, which is used to examine specimens derived from the human body to provide information for the diagnosis, treatment, or prevention of disease. While the input is an image (derived from the body), the software is performing an analysis on that image to provide diagnostic information.
- Device Description: The device description further elaborates on how the software "characterizes image features using the ROI data to generate a categorical output that aligns to BI-RADS categories" and uses "computer vision and machine learning techniques embedded within a software capable of reading, interpreting, analyzing, and generating findings from ultrasound data." This process of analyzing data derived from the body (the ultrasound images) to generate diagnostic findings is a core function of an IVD.
- Predicate Devices: The listed predicate devices, DEN170022 QuantX and K161959 ClearView cCAD, are both known to be IVD devices used for medical image analysis in breast imaging. This further supports the classification of Koios DS for Breast as an IVD.
While the device processes images rather than biological samples in the traditional sense of a lab test, the regulatory definition of an IVD is broad enough to encompass software that analyzes medical images derived from the human body to provide diagnostic information. The key is that the output of the device is intended to aid in the diagnosis of a disease or condition.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The section "Control Plan Authorized (PCCP) and relevant text" explicitly states "Not Found".
Intended Use / Indications for Use
Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who are being referred for further diagnostic ultrasound examination. Koios DS for Breast is a machine learning-based decision support system, indicated as an adjunct to diagnostic ultrasound for breast cancer. Koios DS for Breast automatically classifies user-selected region(s) of interest (ROIs) containing a breast lesion into four BI-RADS-aligned categories (Benign, Suspicious, Probably Malignant), and displays a continuous graphical confidence level indicator of where the lesson falls across all categories. Koios DS for Breast also automatically classifies lesion shape and orientation according to BI-RADS descriptors.
The software requires a user to select up to two orthogonal views, that represent a single lesion to be selected and processed. When utilized by an interpreting physician who has completed training, this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the Koios DS for Breast analysis. This device is intended to help trained interpreting physicians improve their overall accuracy as well as reduce inter- and intra-operator variability.
Koios DS for Breast may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.
Limitations: Koios DS for Breast is not to be used on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. Koios DS for Breast is not intended for the primary interpretation of digital mammography images. Koios DS for Breast is not intended for use on mobile devices.
Product codes
POK
Device Description
Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing breast ultrasound images. The system provides image derived data via web triggering and remote analysis. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS for Breast application, the user examines selected breast ultrasound DICOM images. The user selects up to two Regions of Interest (ROIs) of two orthogonal views of a breast lesion for processing by Koios DS for Breast. The ROI(s) are transmitted electronically to the Koios DS for Breast server for image processing and the results are returned to the user for review.
The Koios DS for Breast core engine characterizes image features using the ROI data to generate a categorical output that aligns to BI-RADS categories. The engine uses computer vision and machine learning techniques embedded within a software capable of reading, interpreting, analyzing, and generating findings from ultrasound data. The underlying engine draws upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model. The categorical output of the Koios DS for Breast engine is divided into four categories (Benign, Probably Benign, Suspicious, Probably Malignant), separated by three operating points, aligning with or exceeding the sensitivity and specificity of radiologist chosen BI-RADS categorizations. . The output of the system is a digital display to be used as a concurrent read. Koios DS for Breast is intended to support compliance with the ACR BI-RADS ultrasound lexicon classification form. The engine additionally classifies the region of interest on the basis of shape (Oval, Round, Irregular) and orientation (Parallel to Skin, Not Parallel).
Koios DS for Breast results can be saved or transferred in three separate ways: in-transit transmission, PACS saving, and exporting results to third-party reporting software. Intransit transmission may be utilized when users wish to share analyses across viewing workstations. Results can be stored in in-transit memory for a preset period of time defined by a system administrator. These results are never locally cached, written to disk, or otherwise stored outside of in-transit memory. After that preset period of time, all results are wiped from the local memory.
Another method of saving is storing a report in the patient series on the PACS. After single or multiple breast lesion analyses have been performed and ultimately accepted by a trained interpreting physician, Koios DS for Breast can export a summary report to PACS as an addendum to the DICOM series that was selected for processing. This report serves as future reference and aid in comparison of cases requiring follow up. This functionality is strictly reserved for approved users.
Koios DS for Breast also supports exporting results to third-party reporting software to facilitate the reporting process. Saving or exporting preferences can be configured by the system administrator and user.
The Koios DS for Breast software is an ASP.NET web application that is deployed to an IIS Web Server inside a Windows operating system environment. The software does not require any specialized hardware, but the time to process ROIs will vary depending on the hardware specifications. If utilizing the recommended technical specifications, the time to generate and present results for two analyzed ROIs will be
§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.
(a)
Identification. A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and recommended user training.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use.
(viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (
e.g., lesion and organ characteristics, disease stages, and imaging equipment).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the text "FDA U.S. FOOD & DRUG ADMINISTRATION" on the right. The symbol on the left is a stylized image of a caduceus, which is a traditional symbol of medicine. The text on the right is in blue and is arranged in three lines.
July 3, 2019
Koios Medical, Inc. % Mr. Lev Barinov VP of Clinical Excellence and Applied Research 500 7th Avenue, 8th Floor NEW YORK NY 10018
Re: K190442
Trade/Device Name: Koios DS for Breast Regulation Number: 21 CFR 892.2060 Regulation Name: Computer-assisted diagnostic software for lesions suspicious for cancer Regulatory Class: Class II Product Code: POK Dated: June 27, 2019 Received: June 28, 2019
Dear Mr. Barinov:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
1
https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K190442
Device Name Koios DS for Breast
Indications for Use (Describe)
Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who are being referred for further diagnostic ultrasound examination. Koios DS for Breast is a machine learning-based decision support system, indicated as an adjunct to diagnostic ultrasound for breast cancer. Koios DS for Breast automatically classifies user-selected region(s) of interest (ROIs) containing a breast lesion into four BI-RADS-aligned categories (Benign, Suspicious, Probably Malignant), and displays a continuous graphical confidence level indicator of where the lesson falls across all categories. Koios DS for Breast also automatically classifies lesion shape and orientation according to BI-RADS descriptors.
The software requires a user to select up to two orthogonal views, that represent a single lesion to be selected and processed. When utilized by an interpreting physician who has completed training, this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the Koios DS for Breast analysis. This device is intended to help trained interpreting physicians improve their overall accuracy as well as reduce inter- and intra-operator variability.
Koios DS for Breast may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.
Limitations: Koios DS for Breast is not to be used on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. Koios DS for Breast is not intended for the primary interpretation of digital mammography images. Koios DS for Breast is not intended for use on mobile devices.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
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5. 510(k) Summarv of Safety and Effectiveness
This 510(k) summary of safety and effectiveness information is submitted as part of the Pre-Market Notification in accordance with the requirements of 21 CFR Part 807. Subpart E and Section 807.92.
1. Identification of Submitter:
| Submitter: | Koios Medical Inc. |
|---|---|
| Address: | 500 7th Avenue |
| 8th Floor | |
| New York, NY 10018 | |
| Phone: | 732-529-5755 |
| Fax: | 732-529-5757 |
| Contact: | Lev Barinov |
| Title: | VP of Clinical Excellence & Applied Research |
| Phone: | 732-529-5755 |
| Fax: | 732-529-5757 |
| Summary Date: | February 21, 2019 |
2. Identification of Product:
Device Name: Koios DS for Breast, Version 2.0 Device Common Name: Radiological Computer-Assisted Diagnostic Software Device Classification: 21 CFR 892.2060, Class II, POK Classification Name: Radiological Computer-Assisted Diagnostic Software (CADx) For Lesions Suspicious For Cancer Manufacturer: Koios Medical, Inc.
3. Marketed Devices
The Koios DS for Breast system is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who have been referred for further diagnostic ultrasound examination. Ultrasound images of the breast should be acquired with a small parts linear array or dedicated breast ultrasound linear array transducers. The system provides a generated categorical output that aligns with or exceeds the sensitivity and specificity of radiologist chosen BI-RADS® categorizations using computer vision and machine learning techniques. In terms of safety and performance, this software medical device is substantially equivalent to the devices listed below:
Model: QuantX Manufacturer: Quantitative Insights, Inc. 510(k) Number: DEN170022
510(k) Summary
4
Model: ClearView cCAD, Version 1.0 Manufacturer: ClearView Diagnostics, Inc. 510(k) Number: K161959
4. Device Description
Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing breast ultrasound images. The system provides image derived data via web triggering and remote analysis. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS for Breast application, the user examines selected breast ultrasound DICOM images. The user selects up to two Regions of Interest (ROIs) of two orthogonal views of a breast lesion for processing by Koios DS for Breast. The ROI(s) are transmitted electronically to the Koios DS for Breast server for image processing and the results are returned to the user for review.
The Koios DS for Breast core engine characterizes image features using the ROI data to generate a categorical output that aligns to BI-RADS categories. The engine uses computer vision and machine learning techniques embedded within a software capable of reading, interpreting, analyzing, and generating findings from ultrasound data. The underlying engine draws upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model. The categorical output of the Koios DS for Breast engine is divided into four categories (Benign, Probably Benign, Suspicious, Probably Malignant), separated by three operating points, aligning with or exceeding the sensitivity and specificity of radiologist chosen BI-RADS categorizations. . The output of the system is a digital display to be used as a concurrent read. Koios DS for Breast is intended to support compliance with the ACR BI-RADS ultrasound lexicon classification form. The engine additionally classifies the region of interest on the basis of shape (Oval, Round, Irregular) and orientation (Parallel to Skin, Not Parallel).
Koios DS for Breast results can be saved or transferred in three separate ways: in-transit transmission, PACS saving, and exporting results to third-party reporting software. Intransit transmission may be utilized when users wish to share analyses across viewing workstations. Results can be stored in in-transit memory for a preset period of time defined by a system administrator. These results are never locally cached, written to disk, or otherwise stored outside of in-transit memory. After that preset period of time, all
510(k) Summary
5
results are wiped from the local memory.
Another method of saving is storing a report in the patient series on the PACS. After single or multiple breast lesion analyses have been performed and ultimately accepted by a trained interpreting physician, Koios DS for Breast can export a summary report to PACS as an addendum to the DICOM series that was selected for processing. This report serves as future reference and aid in comparison of cases requiring follow up. This functionality is strictly reserved for approved users.
Koios DS for Breast also supports exporting results to third-party reporting software to facilitate the reporting process. Saving or exporting preferences can be configured by the system administrator and user.
The Koios DS for Breast software is an ASP.NET web application that is deployed to an IIS Web Server inside a Windows operating system environment. The software does not require any specialized hardware, but the time to process ROIs will vary depending on the hardware specifications. If utilizing the recommended technical specifications, the time to generate and present results for two analyzed ROIs will be 20mm. Size was unavailable for 7 lesions (0.8%). The minimum lesion size was 3mm.
Per the primary endpoint of the study, ROC curves were generated and analyzed. All AUCs were computed via the trapezoidal approximation. Based on the standard error measurements, the error can be propagated to estimate the mean performance interface and 95% confidence interval. This was found to be 0.0370 (0.030, 0.044) at a = .05, satisfying the success criteria for the primary endpoint.
To characterize the effect of Koios DS (USE + DS) system on inter-operator variability, the Kendall Tau-B correlation coefficient was computed in a pairwise manner for all readers. The metric is > 0 for all reader pairs. The standard error for USE + DS and USE Alone was computed to assess if the shifts in the metric were significant. The average Kendall Tau-B of USE Alone was .5404 (.5301, .5507) and the average Kendall Tau-B of
14
USE + DS was .6797 (.6653, .6941) with 95% CI demonstrating a significant increase in the metric (α = . 05).
Also assessed was the effect of Koios DS on intra-operator variability leveraging 150 reads that did not switch from USE Alone to USE + DS across the washout session in the reader study (75 each). USE Alone class switching rate was 13.6% and the USE + DS class switching rate was 10.8% (p = 0.042), demonstrating a statistically significant reduction in intra-reader variability when using USE + DS.
8. Non-Clinical Performance Data
Malignancy Risk Classification:
Bench testing was performed to ascertain the degree of concordance with trained interpreting physicians. Ground truth for malignancy risk classification was determined by pathology or 1-year follow-up for cases that were not biopsied. The system was analyzed on 900 lesions from 900 different patients set aside from the system's training data for the purpose of validating performance. Each lesion is represented by two orthogonal images (e.g. radial and anti-radial), providing a total of 1800 images. System performance on the 900 cases reported an AUC of 88.2%.
BI-RADS Descriptors:
Bench testing was performed to ascertain the degree of concordance with trained interpreting physicians. Ground truth for shape and orientation was supplied by three MQSA certified radiologists. These physicians, all with over 20 years of experience and at least 3000 images read per year, evaluated this dataset on the aforementioned parameters. The system was used to analyze these 1300 cases on the same parameters. In the first test, the system was analyzed on 1204 cases which had a majority decision on shape and 1227 lesions which had a majority decision on orientation. The Koios DS for Breast system was able to achieve overall accuracy that fell within the 95% confidence interval of the radiologists' performance, rendering them statistically equivalent.
15
| Category | Koios DS
for Breast
properly
Identified | Koios DS for
Breast
Proportion | 95% Cl | Cut Off for
Proportion Point
estimate | Conclusion |
|-----------------------|--------------------------------------------------|--------------------------------------|--------------------|---------------------------------------------|---------------------------------------------------------|
| Lesion
orientation | 1125 | 91.12% | [89.43%
92.60%] | 86.12% | Within criteria established
for clinical equivalence |
| Lesion shape | 1066 | 87.62% | [85.68%
89.36%] | 83.54% | Within criteria established
for clinical equivalence |
Koios DS performance for shape and orientation classification, when measured against majority decision
In the second test, categorical agreement between each pair of readers was compared to agreement between each reader and the system. For this test, majority agreement was not enforced and all cases were analyzed for reader and reader-system agreement. Agreement was estimated by means of calculating the Cohen's Kappa coefficient for each pair. On average, for both shape and orientation, the level of agreement between two pairs of readers was not found to statistically differ from the level of agreement between readers and the system.
| к: Reader vs Reader | к: System vs Reader | |
|---|---|---|
| Shape | 0.769 [0.711, 0.826] | 0.738 [0.679, 0.797] |
| Orientation | 0.728 [0.655, 0.801] | 0.744 [0.675, 0.813] |
Average kappa coefficient and 95% confidence intervals for reader and system vs reader categorizations
16
9. Conclusion
Based on the substantial equivalence evaluation, which is derived from the nonclinical and clinical tests that demonstrate that the device is as safe, as effective, and performs as well as or better than the legally marketed device predicates, we have determined that the Koios DS for Breast product is substantially equivalent to DEN170022 and K161959.